Histograms of local intensity differences for pedestrian classification in far-infrared images

Presented is an intensity-based feature extraction method for pedestrian classification in far-infrared (FIR) images. The underlying idea of the method is that only intensity differences between neighbouring pixels can represent both the direction and the magnitude of the gradient, as FIR images are characterised by monotonic grey-level changes. A new intensity-based feature called the histogram of local intensity differences (HLID) is introduced which is a modified version of the well-known histograms of oriented gradients (HOGs) feature. Experiments show that the HLID is more suited to FIR images than HOGs in terms of both accuracy and computational efficiency.